Benchmark scores are the first thing you see in a model announcement — and the last thing you should use to make a production decision. Understanding what these benchmarks measure (and what they don't) is essential for selecting the right model for your use case.
The major benchmarks explained
| Benchmark | What it tests | Format | Weakness |
|---|---|---|---|
| MMLU | Multidomain knowledge (57 subjects) | Multiple choice | Narrow format; high scores don't predict open-ended performance |
| HumanEval | Python code generation from docstrings | Unit test pass rate | Simple, single-function problems — not real codebases |
| MT-Bench | Multi-turn instruction following | LLM-as-judge (1–10) | Judge model bias; not reproducible across judge versions |
| GPQA | Expert-level science questions | Multiple choice | Narrow domain; contamination risk from PhD-level training data |
| MATH | Competition math (AMC, AIME) | Exact match | Doesn't cover applied reasoning in business/scientific contexts |
| SWE-Bench | Real GitHub issue resolution | Automated test pass | Slow and expensive to run; reflects specific codebase patterns |
⚠️ Training contamination is a real problem. Many benchmark datasets were publicly available before the training cutoffs of modern models. High benchmark scores may reflect memorization, not capability.
What benchmarks don't tell you
- Your specific domain: A model scoring 92% on MMLU may underperform a model scoring 85% on your specific finance or legal task.
- Latency and cost: A 95% benchmark score on a $15/MTok model vs. 87% on a $0.15/MTok model — the cheaper model is often the right call at scale.
- Instruction following reliability: Most benchmarks are single-turn. Real-world agent tasks require consistent multi-step instruction following.
- Tool use quality: No public benchmark reliably measures tool-use accuracy for agentic pipelines.
How to actually pick a model
- Run your own evals on 50–100 representative examples from your actual task.
- Measure latency and cost per successful output — not just accuracy.
- Test multi-turn consistency if your application requires it.
- Use public benchmarks as a coarse filter to eliminate clearly unsuitable models, then run task-specific evals to make the final decision.
Task-specific evals on MoltBot
Run your own golden dataset evals against any model. Compare accuracy, cost, and latency side-by-side. 14-day free trial.
Start Free Trial →